%A Wei Wang %A Florent Masseglia %A Thomas Guyet %A Rene Quiniou %A Marie-Odile Cordier %T A General Framework for Adaptive and Online Detection of Web Attacks %X Detection of web attacks is an important issue in current defense-in-depth security framework. In this paper, we pro- pose a novel general framework for adaptive and online de- tection of web attacks. The general framework can be based on any online clustering methods. A detection model based on the framework is able to learn online and deal with ?con- cept drift? in web audit data streams. Str-DBSCAN that we extended DBSCAN [1] to streaming data as well as StrAP [3] are both used to validate the framework. The detec- tion model based on the framework automatically labels the web audit data and adapts to normal behavior changes while identifies attacks through dynamical clustering of the streaming data. A very large size of real HTTP Log data col- lected in our institute is used to validate the framework and the model. The preliminary testing results demonstrated its effectiveness. %C Madrid, Spain %D 2009 %P 1141-1141 %L www2009151